scholarly journals Evaluation of Aboveground Nitrogen Content of Winter Wheat Using Digital Imagery of Unmanned Aerial Vehicles

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4416 ◽  
Author(s):  
Baohua Yang ◽  
Mengxuan Wang ◽  
Zhengxia Sha ◽  
Bing Wang ◽  
Jianlin Chen ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Unmanned Aerial Vehicles ◽  
Nitrogen Content ◽  
Spatial Information ◽  
Vegetation Indices ◽  
Scale Transformation ◽  
Support Vector ◽  
Spatial Feature ◽  
Aerial Vehicles ◽  
Wavelet Features

Nitrogen (N) content is an important basis for the precise management of wheat fields. The application of unmanned aerial vehicles (UAVs) in agriculture provides an easier and faster way to monitor nitrogen content. Previous studies have shown that the features acquired from UAVs yield favorable results in monitoring wheat growth. However, since most of them are based on different vegetation indices, it is difficult to meet the requirements of accurate image interpretation. Moreover, resampling also easily ignores the structural features of the image information itself. Therefore, a spectral-spatial feature is proposed combining vegetation indices (VIs) and wavelet features (WFs), especially the acquisition of wavelet features from the UAV image, which was transformed from the spatial domain to the frequency domain with a wavelet transformation. In this way, the complete spatial information of different scales can be obtained to realize good frequency localization, scale transformation, and directional change. The different models based on different features were compared, including partial least squares regression (PLSR), support vector regression (SVR), and particle swarm optimization-SVR (PSO-SVR). The results showed that the accuracy of the model based on the spectral-spatial feature by combining VIs and WFs was higher than that of VIs or WF indices alone. The performance of PSO-SVR was the best (R2 = 0.9025, root mean square error (RMSE) = 0.3287) among the three regression algorithms regardless of the use of all the original features or the combination features. Our results implied that our proposed method could improve the estimation accuracy of aboveground nitrogen content of winter wheat from UAVs with consumer digital cameras, which have greater application potential in predicting other growth parameters.

scholarly journals Learning-Based Autonomous UAV System for Electrical and Mechanical (E&M) Device Inspection

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1385
Author(s):  
Yurong Feng ◽  
Kwaiwa Tse ◽  
Shengyang Chen ◽  
Chih-Yung Wen ◽  
Boyang Li
Keyword(s):  
System Design ◽  
State Estimation ◽  
Unmanned Aerial Vehicles ◽  
Visual Inspection ◽  
Spatial Information ◽  
Free Flight ◽  
Object State ◽  
Aerial Vehicles ◽  
Inspection Tasks ◽  
Autonomous Inspection

The inspection of electrical and mechanical (E&M) devices using unmanned aerial vehicles (UAVs) has become an increasingly popular choice in the last decade due to their flexibility and mobility. UAVs have the potential to reduce human involvement in visual inspection tasks, which could increase efficiency and reduce risks. This paper presents a UAV system for autonomously performing E&M device inspection. The proposed system relies on learning-based detection for perception, multi-sensor fusion for localization, and path planning for fully autonomous inspection. The perception method utilizes semantic and spatial information generated by a 2-D object detector. The information is then fused with depth measurements for object state estimation. No prior knowledge about the location and category of the target device is needed. The system design is validated by flight experiments using a quadrotor platform. The result shows that the proposed UAV system enables the inspection mission autonomously and ensures a stable and collision-free flight.

A Method for Estimating the Area of Damaged Croplands and Woodlands with the Use of Unmanned Aerial Vehicles (Drones)

2017 ◽  
Author(s):  
Tomasz Podciborski ◽  
Jacek Kil
Keyword(s):  
Natural Disasters ◽  
Unmanned Aerial Vehicles ◽  
Spatial Information ◽  
Rapid Development ◽  
Spatial Development ◽  
Measurement Methods ◽  
Loss Assessment ◽  
Aerial Vehicles ◽  
Extent Of Damage

Growing social demand for access to spatial information spurs the rapid development of measurement methods and systems for registering the results of spatial evaluations and analyses (Kwietniewski 2008). Any assessment of spatial development is carried out on the basis of information obtained from specific sources (Kowalczyk 2007). The main objective of this study was to propose a method for assessing the extent of damage caused by natural disasters to croplands and woodlands with the use of unmanned aerial vehicles (drones). The main aim was achieved through detailed goals, including determination of the causes of natural disasters, description of the field inspection procedure and development of loss assessment principles. The proposed method was verified in selected research sites, and the resulting damage report detailing cropland losses is presented in the study.

scholarly journals Detecting Individual Tree Attributes and Multispectral Indices Using Unmanned Aerial Vehicles: Applications in a Pine Clonal Orchard

2020 ◽  
Vol 12 (24) ◽  
pp. 4144
Author(s):  
José Luis Gallardo-Salazar ◽  
Marín Pompa-García
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Field Measurements ◽  
Tree Level ◽  
Individual Tree ◽  
Basal Diameter ◽  
Crown Diameter ◽  
Aerial Vehicles

Modern forestry poses new challenges that space technologies can solve thanks to the advent of unmanned aerial vehicles (UAVs). This study proposes a methodology to extract tree-level characteristics using UAVs in a spatially distributed area of pine trees on a regular basis. Analysis included different vegetation indices estimated with a high-resolution orthomosaic. Statistically reliable results were found through a three-phase workflow consisting of image acquisition, canopy analysis, and validation with field measurements. Of the 117 trees in the field, 112 (95%) were detected by the algorithm, while height, area, and crown diameter were underestimated by 1.78 m, 7.58 m2, and 1.21 m, respectively. Individual tree attributes obtained from the UAV, such as total height (H) and the crown diameter (CD), made it possible to generate good allometric equations to infer the basal diameter (BD) and diameter at breast height (DBH), with R2 of 0.76 and 0.79, respectively. Multispectral indices were useful as tree vigor parameters, although the normalized-difference vegetation index (NDVI) was highlighted as the best proxy to monitor the phytosanitary condition of the orchard. Spatial variation in individual tree productivity suggests the differential management of ramets. The consistency of the results allows for its application in the field, including the complementation of spectral information that can be generated; the increase in accuracy and efficiency poses a path to modern inventories. However, the limitation for its application in forests of more complex structures is identified; therefore, further research is recommended.

scholarly journals Developing Inspection Methodology of Solar Energy Plants by Thermal Infrared Sensor on Board Unmanned Aerial Vehicles

Energies ◽  
2019 ◽  
Vol 12 (15) ◽  
pp. 2928 ◽  
Author(s):  
Dong Ho Lee ◽  
Jong Hwa Park
Keyword(s):  
Power Generation ◽  
Unmanned Aerial Vehicles ◽  
Spatial Information ◽  
Thermal Infrared ◽  
Normal Operation ◽  
Infrared Sensor ◽  
Infrared Sensors ◽  
Solar Module ◽  
Inspection Method ◽  
Aerial Vehicles

Photovoltaic (PV) power generation facilities have been built on various scales due to rapid growth in response to demand for renewable energy. Facilities built on diverse terrain and on such a scale are required to employ fast and accurate monitoring technology for stable electrical production and maintenance. The purpose of this study was to develop a technology to analyze the normal operation and failure of solar modules by acquiring images by attaching optical and thermal infrared sensors to unmanned aerial vehicles (UAVs) and producing orthographic images of temperature information. The results obtained in this study are as follows: (1) a method of using optical and thermal infrared sensors with different resolutions at the same time is able to produce accurate spatial information, (2) it is possible to produce orthographic images of thermal infrared images, (3) the analysis of the temperature fluctuation characteristics of the solar panel and cell showed that the abnormal module and cell displayed a larger temperature change than the normal module and cell, and (4) the abnormal heat generation of the panel and cell can be accurately discerned by the abnormal state panel and cell through the spatial distribution of the temperature. It is concluded that the inspection method of the solar module using the obtained UAV-based thermal infrared sensor can be useful for safety inspection and monitoring of the rapidly growing solar power generation facility.

scholarly journals Environment Classification for Unmanned Aerial Vehicle Using Convolutional Neural Networks

2020 ◽  
Vol 10 (14) ◽  
pp. 4991
Author(s):  
Carlos Villaseñor ◽  
Alberto A. Gallegos ◽  
Javier Gomez-Avila ◽  
Gehová López-González ◽  
Jorge D. Rios ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Computational Cost ◽  
Support Vector ◽  
Normal Amount ◽  
Human Expert ◽  
Aerial Vehicles ◽  
Training Samples ◽  
Aerial Vehicle ◽  
Water Accumulation ◽  
Low Computational Cost

Environment classification is one of the most critical tasks for Unmanned Aerial Vehicles (UAV). Since water accumulation may destabilize UAV, clouds must be detected and avoided. In a previous work presented by the authors, Superpixel Segmentation (SPS) descriptors with low computational cost are used to classify ground, sky, and clouds. In this paper, an enhanced approach to classify the environment in those three classes is presented. The proposed scheme consists of a Convolutional Neural Network (CNN) trained with a dataset generated by both, an human expert and a Support Vector Machine (SVM) to capture context and precise localization. The advantage of using this approach is that the CNN classifies each pixel, instead of a cluster like in SPS, which improves the resolution of the classification, also, is less tedious for the human expert to generate a few training samples instead of the normal amount that it is required. This proposal is implemented for images obtained from video and photographic cameras mounted on a UAV facing in the same direction of the vehicle flight. Experimental results and comparison with other approaches are shown to demonstrate the effectiveness of the algorithm.

Sign in / Sign up

Export Citation Format

Share Document